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Found 13 papers, 0 papers with code
Joint Precoding and Fronthaul Compression for Cell-Free MIMO Downlink With Radio Stripes
Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it.
Learning Decentralized Power Control in Cell-Free Massive MIMO Networks
This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination.
A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization
However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i. e., the number of antennas or users.
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission
This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs).
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for Cell-Free MIMO Systems
Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by splitting the message for each user equipment (UE) into a private message and a set of common messages, which are simultaneously transmitted by means of superposition coding.
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity
A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource.
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies.
Joint Secure Design of Downlink and D2D Cooperation Strategies for Multi-User Systems
This work studies the role of inter-user device-to-device (D2D) cooperation for improving physical-layer secret communication in multi-user downlink systems.
Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency
Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers.
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures.
Learning Robust Beamforming for MISO Downlink Systems
This paper investigates a learning solution for robust beamforming optimization in downlink multi-user systems.
Deep Learning Methods for Universal MISO Beamforming
This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station.
Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels.
Signal Processing Information Theory Information Theory
